Department of Environmental Health, Boston University School of Public Health, Boston, Mass.
Boston Medical Center, Boston, Mass; Department of Pediatrics, Boston University School of Medicine, Boston, Mass.
J Allergy Clin Immunol. 2021 Jun;147(6):2162-2170. doi: 10.1016/j.jaci.2020.11.045. Epub 2020 Dec 15.
Extensive data available in electronic health records (EHRs) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can be accomplished only when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent.
Our aims were to create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies and also to examine the presence and absence of these data in relation to patient characteristics.
We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity.
The asthma severity computable phenotype performs as expected in comparison with national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present.
We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs.
电子健康记录（EHR）中广泛的数据有可能改善哮喘护理，并深入了解影响哮喘结果的因素。然而，只有当 EHR 数据能够准确衡量严重程度时，才能完成这项工作，而目前严重程度的衡量方法既复杂又不一致。
我们的目标是创建和评估一个基于临床哮喘指南的标准化儿科哮喘严重程度表型，用于基于 EHR 的健康计划和研究，同时还研究这些数据在与患者特征的关系中的存在和缺失情况。
我们开发了一个哮喘严重程度可计算表型，并比较了不同严重程度成分与文献趋势的一致性。我们使用多变量逻辑回归来评估与哮喘严重程度相关的 EHR 数据的存在情况。
与国家统计数据和文献相比，哮喘严重程度可计算表型的表现符合预期。基于长期药物治疗方案成分对儿童进行严重程度分类的最大化，而仅基于症状数据成分的最小化。使用严重程度表型可实现更好、更具临床意义的分类。从这些 EHR 数据中可以确定严重程度的儿童更有可能在门诊接受哮喘治疗，而不太可能年龄较大或为西班牙裔。黑人儿童不太可能有肺功能测试数据。
我们开发了一种实用的儿科哮喘严重程度可计算表型，可移植到其他 EHR 中。
